Vetted AWS Professionals

“Aspiring AI product builder interested in LLMs and deep learning, exploring forming a team (including fresh graduates) and leveraging crowdsourcing to develop ideas. Has not raised capital and has no VC/accelerator experience yet, but is thinking ahead about funding needs and partnering with an operational co-founder while potentially joining an existing team.”

“End-to-end product engineer who built and shipped Shopify Magic, an LLM-powered product-description generator on Amazon Bedrock with RAG over a tenant-isolated vector database, achieving 50% faster content creation, sub-2s latency, and 70%+ merchant adoption. Also led a Flexport migration from a monolithic Rails app to microservices using feature flags and parallel runs, delivering zero downtime and a 60% improvement in development speed.”

“Amazon Prime Video backend engineer who built and operated high-traffic Python/FastAPI services and AWS-native data/batch systems. Demonstrates strong production reliability and incident ownership (CloudWatch/X-Ray), plus measurable performance wins (8s to <200ms query latency, ~40% CPU reduction) and cost-focused architectures (Lambda + ECS/Fargate with Fargate Spot).”

“Robotics software engineer with extensive ROS2 academic project experience (UMDCP), including a drone-based 3D object reconstruction system using Mast3r where they built ROS2 nodes for autonomous image capture, containerized the ROS2/OpenCV stack for hardware deployment, and automated AWS uploads/compute-triggered reconstruction. Demonstrated strong sim-to-real debugging using ROS bags and PlotJuggler to correct yaw/trajectory offsets, and built multi-node TurtleBot navigation using visual cues (horizon/stop signal/obstacle detection) feeding a cmd_vel controller.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”

“Backend/platform engineer with deep AWS experience who built a high-reliability ingestion platform (Lambda + Step Functions + DynamoDB) that became the single source of truth for training/qualification certification data across an AU region, handling high-volume async updates with strong consistency controls. Also led a major API migration from Lambda to ECS/Fargate to eliminate cold starts and increase throughput, and has hands-on EKS/Kubernetes operations plus Kafka partitioning/ordering expertise.”

“Technically oriented builder focused on taking ambiguous LLM/agentic workflow needs from scoped problem definition to iterative prototypes, with emphasis on edge cases and customer feedback. Has hands-on experience debugging RAG/agent systems, including resolving a real integration issue involving search query quoting differences between manual search and an API, and is comfortable fielding deep developer Q&A in demos.”

“Product/operations-focused builder exploring an AI-based vacation planning app (early-stage). Previously drove a large-scale fulfillment-center automation initiative impacting 1.2M associates, reducing peak-season ad-hoc hiring and delivering ~$39MM in annual savings through a metrics-driven, cross-functional execution approach.”